diff --git a/library/train_util.py b/library/train_util.py
index 07c253a0..8e220e80 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -34,6 +34,7 @@ import toml
 # from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from tqdm import tqdm
+from packaging.version import Version
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
@@ -5077,17 +5078,18 @@ def prepare_accelerator(args: argparse.Namespace):
     if args.torch_compile:
         dynamo_backend = args.dynamo_backend
 
-    kwargs_handlers = (
-        InitProcessGroupKwargs(timeout=datetime.timedelta(minutes=args.ddp_timeout)) if args.ddp_timeout else None,
-        (
-            DistributedDataParallelKwargs(
-                gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, static_graph=args.ddp_static_graph
-            )
-            if args.ddp_gradient_as_bucket_view or args.ddp_static_graph
-            else None
-        ),
-    )
-    kwargs_handlers = list(filter(lambda x: x is not None, kwargs_handlers))
+    kwargs_handlers = [
+        InitProcessGroupKwargs(
+            backend = "gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
+            init_method="env://?use_libuv=False" if os.name == "nt" and Version(torch.__version__) >= Version("2.4.0") else None,
+            timeout=datetime.timedelta(minutes=args.ddp_timeout) if args.ddp_timeout else None
+        ) if torch.cuda.device_count() > 1 else None,
+        DistributedDataParallelKwargs(
+            gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, 
+            static_graph=args.ddp_static_graph
+        ) if args.ddp_gradient_as_bucket_view or args.ddp_static_graph else None
+    ]
+    kwargs_handlers = [i for i in kwargs_handlers if i is not None]
     deepspeed_plugin = deepspeed_utils.prepare_deepspeed_plugin(args)
 
     accelerator = Accelerator(